Evolving conceptions of the role of large dams in social-ecological resilience
Bibliographic record
Abstract
Rivers and riparian ecosystems have historically provided a range of beneficial goods and services to human societies. However, floodplains have also posed risks to the humans that came to rely upon them. Although riparian areas are among the most resource-rich and biodiverse ecosystems, they are also some of the most disturbed by human activity. Today, social and economic needs for water diverted off-stream are often pitted against the flow of water needed to maintain crucial instream ecological functions. The construction of dams has been a widely implemented method to control rivers for human purposes, particularly in the western United States. However, there is a growing movement to decommission dams, as stakeholders begin to recognize the ultimate value of restoring ecosystem services, including cultural ecosystem services; indeed, their restoration may be necessary to ensure lasting systemic resilience. Broader questions of dam decommissioning in the United States are receiving increasing attention by scholars and practitioners alike. In this paper, we adapt and apply seminal concepts from the adaptive cycle framework and cultural ecosystem services, particularly for Native Nations, and thereby assess the unfolding case of decommissioning and restoration on the Elwha River in northwest Washington State. The empirical evidence indicates that dam removal coincided with scalar and temporal alignment of multiple adaptive cycles and contributed to both short and long-term resilience. Further, the Elwha case represents an extremely important precedent in the evolution of river management practices, in which stakeholder-based collaborative governance incorporated knowledge coproduction and regulatory maneuvering to successfully overcome obstacles inherent in both dam decommissioning and subsequent restoration. We conclude by reflecting on lessons of broader relevance beyond the specific case of the Elwha.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.067 |
| Scholarly communication | 0.008 | 0.017 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".